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Frequent Itemset Mining

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Abstract

We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators. Even though our survey certainly is not exhaustive, it covers essential reference material, since we believe that an attempt to cover everything will instead fail to convey any useful information to the interested readers. Our hope is that this work will help interested researchers and practitioners, in particular those coming from a business-oriented background, quickly enabling them to develop their understanding of an area likely to play an ever more significant role in coming years.

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Authors and Affiliations

  1. University of Salento, Lecce, Italy

    Massimo Cafaro & Marco Pulimeno

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  1. Massimo Cafaro
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  2. Marco Pulimeno
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Correspondence to Massimo Cafaro .

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Editors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Pablo Moscato

  2. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Natalie Jane de Vries

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Cafaro, M., Pulimeno, M. (2019). Frequent Itemset Mining. In: Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas. Springer, Cham. https://doi.org/10.1007/978-3-030-06222-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-06222-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-06221-7

  • Online ISBN: 978-3-030-06222-4

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